The following relates to the customer service arts, call center operational arts, and related arts.
Call centers are common tools by which companies, governmental entities, and the like provide customer support, client support, or other assistance. Telephonic call centers are currently most common, but call centers employing other communication media are increasingly employed, such as Internet-driven text-based online chat. Besides problem diagnosis/resolution, call centers are also used for outreach—for example, telephonic product marketing and political campaigning provides tools for reaching customers and informing potential voters, respectively. In these latter applications, the call center agent initiates the call.
The staffing of call centers can be problematic. In problem diagnosis/resolution tasks, the call center is often expected to provide 24/7 response at any time of day and any day of the week, including weekends and holidays. On the other hand, the actual call load can vary widely depending upon time of day, day of week, or season or time of year. Depending upon the geographical area serviced by the call center, the call load may include multiple time zones, and service in multiple languages may be provided. In outreach tasks, call center activity may need to ramp up quickly, for example to encourage voting shortly before the day of an election.
Further, call center agents must have a multiplicity of skills, including both language/communication skills to effectively communicate and the technical skill to provide the required problem diagnosis and resolution. In the case of outreach tasks, call center agents should be knowledgeable about a product being marketed, or about a political candidate's views on issues being debated in a political campaign.
For some call centers, it may not be cost-effective, or even possible, to provide enough human call agents with the requisite communications and technical skills to fully staff the call center. A known way to address this problem is to provide a computerized call center dialog manager. Such a computerized dialog manager provides guidance for the substantive content of the call dialog. This can be open-loop, e.g. providing a dialog script that the call center agent is expected to follow. More sophisticated call center dialog managers employ feedback control by providing suggested questions for the call center agent to ask based on the dialog history.
In terms of substantive content, a computerized call center dialog manager can be effective, especially when feedback control is employed, since calls commonly follow a limited number of substantive paths. For a given product, there are a limited number of common failure modes, and each failure mode can be diagnosed and resolved using a common set of questions asked in a logical sequence. Similarly, the range of questions that a telephone marketer or political campaign outreach agent is likely to field is relatively small and can be determined, for example, by statistically analyzing a representative number of past calls handled by a skilled human call center agent.
On the other hand, the communication skills aspect of call center automation may be more challenging than the substantive content aspect. Communication skills require adept handling of natural language (spoken in the case of a telephone call, or written in the case of an online chat call), which effectively emulate the natural nuances achieved by human speakers who are fluent in the employed natural language. For example, the call center dialog manager may suggest asking the customer whether the television is plugged in (for example, based on past dialog substance indicating the television is completely nonresponsive)—but there are many ways to actually ask this question. One way: “Did you remember to plug the television into the electrical outlet?” may be interpreted by the customer as insulting, whereas another way “By the way, the television is plugged in, right?” may be interpreted as an amusing joke which nonetheless executes the desired question. Effective natural language communication also exhibits a certain spontaneity or dynamic variability that is difficult to quantify—but a human listener readily perceives, and is often annoyed by, the failure of a speaker to employ appropriate dynamic variability during a spoken or textual dialog.
One way to deal with the communication skills aspect is to employ human call center agents who are fluent in the natural language of the call, in conjunction with a computerized call center dialog manager. In this approach, the human call center agent performs the “translation” of questions suggested by the dialog manager into appropriate natural language utterances. This approach advantageously facilitates employment of call center agents who may lack the requisite technical skill (since this is provided by the computerized dialog manager), but cannot enable full automation. It still may be difficult to hire enough human call center agents with the requisite communication skills even after relaxing the technical skill requirement.
Improved efficiency can be obtained if the call center automation also provides the actual utterances to be asked, that is, the natural language “translation” of the substantive content provided by the dialog manager. In this way, completely automated call center operations can be achieved (except perhaps for the occasional complex or non-standard call, which may be transferred to a human agent). Alternatively, human call center agents may still be employed to speak the suggested utterances, with the automatically suggested utterances providing assistance to human call center agents who may not be fluent in the natural language used in a call.